Leïla Kosseim
Radboud Winkels
Randy Goebel
Start - 2018 | End 2024 Duration - 6 years
The objective of this Subproject is to analyze the contribution of algorithmic tools capable of predicting the probable outcome of a trial. The first step towards making them useful tools for practitioners is to understand the functioning of these tools in order to be able to account for their reliability.
Subproject chief
Kevin Ashley
Research activities
Case studies
The JusticeBot project (Procezeus). This chatbot project is originally aimed at non-attorneys. The researchers intend to develop a rule-tree for the domain, which would more naturally be aimed at attorneys.
This case study establishes a link Subprojects 1 and 2.
The Case summarization project. A machine learning algorithm will be trained to generate summaries based on a unique training package consisting of a set of full case reports and summaries prepared by expert humans.
Publications of our researchers
- Hannes WESTERMANN, Vern R. WALKER, Kevin D. ASHLEY and Karim BENYEKHLEF. "Using Factors to Predict and Analyze Landlord Tenant Decisions to Increase Access to Justice", (2019) 10 pages https://doi.org/10.1145/3322640.3326732.
- Hannes WESTERMANN, Jaromír ŠAVELKA, Vern R. WALKER, Kevin D. ASHLEY et Karim BENYEKHLEF, "Computer-Assisted Creation of Boolean Search Rules for Text Classification in the Legal Domain", (2019) Proceedings of JURIX 2019 10 pages http://ebooks.iospress.nl/volumearticle/53660.
Presentations
- Cassandra LAROCQUE-RIGNEY, Karl BRANTING, Kevin ASHLEY, Tom VAN ENGERS, "Web conference | AI'S Contribution to the Administration of Justice", virtual program of the Cyberjustice Laboratory, June 15, 2020.
Partners
|
Academic partner |
 Institutional partner |
Institutional partner |
|
Industrial partner |
Industrial partner |
Industrial partner |
Industrial partner |
This content has been updated on 14 September 2020 at 8 h 59 min.